Hardware-friendly network quantization (e.g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones. However, due to the discreteness of low-bit quantization, existing quantization methods often face the unstable training process and severe performance degradation. To address this problem, in this paper we propose Differentiable Soft Quantization (DSQ) to bridge the gap between the full-precision and low-bit networks. DSQ can automatically evolve during training to gradually approximate the standard quantization. Owing to its differentiable property, DSQ can help pursue the accurate gradients in backward propagation, and reduce the quantization loss in forward process with an appropriate clipping range. Extensive experiments over several popular network structures show that training lowbit neural networks with DSQ can consistently outperform state-of-the-art quantization methods. Besides, our first efficient implementation for deploying 2 to 4-bit DSQ on devices with ARM architecture achieves up to 1.7× speed up, compared with the open-source 8-bit high-performance inference framework NCNN [31].

Abstract. Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available 1 . In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version 2 . DetectionIn data association based MOT, the tracking performance is heavily affected by the detection results. We implement our detector based on Faster R-CNN [14]. In our implementation, the CNN model is fine-tuned from the VGG-16 on ImageNet. In considering the definition of MOTA in MOT16 [12], the sum of false negatives (FN) and false positives (FP) poses a large impact on the value of MOTA. In Table 1, we show that our detection optimization strategies lead to the significant decrease in the sum of FP and FN 3 .1 https://drive.google.com/open?id=0B5ACiy41McAHMjczS2p0dFg3emM 2 We use POI to denote our online tracker and KDNT to denote our offline tracker in submission. 3 We use detection score threshold 0.3 for Faster R-CNN and -1 for DPMv5 , labeling the ID of detection box with incremental integer, and evaluate FP and FN with MOT16 devkit.

Model binarization is an effective method of compressing neural networks and accelerating their inference process, which enables state-of-the-art models to run on resource-limited devices. Recently, advanced binarization methods have been greatly improved by minimizing the quantization error directly in the forward process. However, a significant performance gap still exists between the 1-bit model and the 32-bit one. The empirical study shows that binarization causes a great loss of information in the forward and backward propagation which harms the performance of binary neural networks (BNNs), and the limited information representation ability of binarized parameter is one of the bottlenecks of BNN performance. We present a novel Distributionsensitive Information Retention Network (DIR-Net) to retain the information of the forward activations and backward gradients, which improves BNNs

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